CTNF 19/183,538 CTNF 100592 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Priority 02-26 AIA Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Specification 06-31 AIA The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification. Status of Application Claims 1-20 are pending. Claims 1, 10, and 19 are independent. This NON-FINAL action is in response to communications received 23 May 2025. Claim Objections 07-29-01 AIA Claim s 1 and 10 are objected to because of the following informalities: Claim 1 – “obtaining, by one or more processors, flight data from one or more aircraft sensors, the flight data associated with a flight operation of an aircraft” introduces antecedent issues with “an aircraft” of the claim preamble. Correct to “obtaining, by one or more processors, flight data from one or more aircraft sensors, the flight data associated with a flight operation of the aircraft”. Claim 10 – ““obtain flight data from one or more aircraft sensors, the flight data associated with a flight operation of an aircraft” introduces antecedent issues with “an aircraft” of the claim preamble. Correct to “obtain flight data from one or more aircraft sensors, the flight data associated with a flight operation of the aircraft” . Appropriate correction is required. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. 101 Analysis – Step 1 Claim 1 is directed to a process. Therefore, Claim 1 is within at least one of the four statutory categories. Claim 10 is directed to an apparatus. Therefore, Claim 10 is within at least one of the four statutory categories. Claim 19 is directed to an apparatus. Therefore, Claim 19 is within at least one of the four statutory categories. 101 Analysis – Step 2A, Prong I Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. Claims 1, 10, and 19 include limitations that recite an abstract idea (emphasized below) and Claim 10 will be used as a representative claim for the remainder of the 101 rejections. Claim 10 recites: A system for assessing performance of an aircraft, the system comprising: a memory ; and processing circuitry coupled to the memory and configured to : obtain flight data from one or more aircraft sensors, the flight data associated with a flight operation of an aircraft ; extract from the flight data, one or more flight data indicators, wherein each of the one or more flight data indicators represent performance of the aircraft during one or more flights; correlate, by a machine learning model, the one or more flight data indicators with one or more operational factors ; generate, by the machine learning model, a custom performance model for the aircraft based on the correlating of the one or more flight data indicators with the one or more operational factors ; output a performance factor for the aircraft based on the custom performance model . The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. Specifically, the “obtain”, “extract”, “correlate”, and “generate” steps encompass a user to gather information and generate a model based on gathered information. Accordingly, the claim recites at least one abstract idea. 101 Analysis – Step 2A, Prong II Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”): For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application. Regarding the additional limitations of “processing circuitry…configured to”, the examiner submits that these limitations are an attempt to generally link additional elements to a technological environment. In particular, the “processing circuitry” is recited at a high level of generality and merely automates the obtaining, extracting, correlating, and generating steps, therefore acting as a generic computer to perform the abstract idea. Additionally, the circuitry is claimed generically and are operating in their ordinary capacity and do not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. The additional limitations are no more than mere instructions to apply the exception using processing circuitry. Furthermore, the examiner submits that the recitations of extracting flight data indicators, correlating flight data indicators, and generating a performance model is a mere definition that does not necessarily impose any meaningful limits on performing the steps in the human mind, as it only compares data where a user could in fact perform this mentally or using paper and pencil. In addition to that, the examiner submits that obtaining flight data and using processing circuitry, are insignificant extra-solution activities that merely use processing circuitry to perform the process. In particular, the obtaining steps are recited at a high level of generality (i.e. as a general means of gathering data for use in the determining step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a controller or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. 101 Analysis – Step 2B Regarding Step 2B of the 2019 PEG, representative independent Claim 10 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of the apparatus, the controller amounts to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations of obtaining data, extracting flight indicators, and correlating flight data, the examiner submits that these limitations are insignificant extra-solution activities. Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The additional limitations of receiving the data and determining errors are well-understood, routine, and conventional activities because the background recites that the sensors from which the data is acquired/received are all conventional sensors. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well ‐ understood, routine, and conventional function when it is claimed in a merely generic manner. Hence, Claim 10 is not patent eligible. Further Claims 1 and 19 are not patent eligible for the same reasons. Dependent Claims 2-9, 11-18, and 20 when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. The additional elements, if any, in the dependent claims are not sufficient to amount to significantly more than the judicial exception for the same reasons as with Claims 1, 10, and 19. Office Note : In order to overcome this rejection, the Office suggests further defining the limitations of the independent claims, for example linking the claimed subject matter to a non-generic device and controlling a vehicle with generated custom performance model. Limitations such as these suggested above would further bring the claimed subject matter out of the realm of abstract idea and into the realm of a statutory category. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15 AIA Claim s 1, 3-10, and 12-19 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Gibbons et al. (US 20210383706 A1), hereinafter Gibbons . Regarding claim 1, Gibbons discloses: A computer-implemented method for assessing performance of an aircraft, the computer-implemented method comprising (Abstract, Systems, apparatuses, and methods for more effectively providing pilots with optimal suggested route or trajectory changes during flight.): obtaining, by one or more processors, flight data from one or more aircraft sensors, the flight data associated with a flight operation of an aircraft ([0074], The collected data may be obtained from on-board sensors (e.g., airspeed, wind resistance, wind velocity, drag, etc.), ground-based systems (e.g., weather conditions, trajectory, etc.), satellites, or airlines records, for example. Once a suitable aircraft performance model has been developed for a specific type or class of aircraft (such as a Boeing 747), that model may be integrated with the TASAR system to provide a more accurate means of flight or trajectory planning for that type or class of aircraft. Such a model for a type or class of aircraft may be created for a plurality of types or classes, i.e., multiple airframes from each of several manufacturers.); extracting, by the one or more processors, from the flight data, one or more flight data indicators, wherein each of the one or more flight data indicators represent performance of the aircraft during one or more flights (Abstract, The collected operational and performance data may be used as input data or “features” for a machine learning algorithm to generate a parameter of a performance model; [0023], collecting operational and performance data for each of a plurality of aircraft); correlating, by a machine learning model, the one or more flight data indicators with one or more operational factors (Abstract, The collected operational and performance data may be used as input data or “features” for a machine learning algorithm to generate a parameter of a performance model; [0024], training a machine learning model to output a parameter of an aircraft performance model from an input to the model, the input comprising operational or performance data for a different aircraft.); generating, by the machine learning model, a custom performance model for the aircraft based on the correlating of the one or more flight data indicators with the one or more operational factors ([0009], In some embodiments, deviations from the performance “predicted” or expected using a baseline aircraft performance model may be determined for an individual aircraft (such as flight time, fuel consumption, drag, lift, etc.). The deviations may be used in a process to update or revise the baseline performance model used in trajectory planning for that aircraft or for a similar set of aircraft. This enables TASAR to more accurately “predict” flight performance and provide more effective route planning. In some embodiments, collection of a suitable set of data from multiple aircraft and the training of a machine learning model may enable the system described herein to identify the characteristics of an aircraft that have the most significant impact on the baseline model parameters, and hence on the trajectory planning process.); and outputting, by the one or more processors, a performance factor for the aircraft based on the custom performance model ([0024], output a parameter of an aircraft performance model from an input to the model, the input comprising operational or performance data for a different aircraft). Regarding claim 3, Gibbons discloses: wherein the performance factor comprises at least one of: estimated flight time of the aircraft, evaluation of fuel efficiency of the aircraft, identification of one or more safety risks of the aircraft, and analysis of operational trends of the aircraft ([0073], performance parameters (such as fuel consumption, lift, drag, etc.)). Regarding claim 4, Gibbons discloses: wherein the performance factor forecasts potential aircraft performance issues based on current trends and the one or more operational factors ([0067], By collecting information for a sufficient number of aircraft of the same airframe type (or having other characteristic(s) common to the set of aircraft), a machine learning model can be trained to predict a different aircraft's fuel milage performance (or other characteristic) based on a set of features. For example, a set of data may be collected for each aircraft of the same manufacturer and airframe model (such as a Boeing 747) that includes information on multiple aspects of each aircraft (type of routes flown, miles flown, years in service, performed maintenance, etc.) and its performance (fuel mileage, frequency of repair, nature of repairs, service issues, etc.). A set of this data for multiple aircraft can be used as training data for a machine learning (Mir) model or models. In some examples, the data may be for aircraft of different types that have similar characteristics (such as wingspan, weight, operating altitude, etc.); [0068], Each ML model may be trained to output a prediction or expected value of a specific characteristic of an aircraft whose feature data is used as an input to the trained model. The output may be, for example, predicted fuel milage performance, expected time to next maintenance, expected cost of operating per mile flown, etc. For example a trained model might be used to “predict” how the performance of an individual aircraft or of a set of aircraft would be expected to change over a specific time period or based on the number of miles flown, the number of takeoffs and landings, etc. As another example, a model might be trained to predict the expected drag coefficient for an airframe based on age and/or miles flown. The features on which a ML model is trained may be a subset of the data collected for a group of aircraft. This subset may be those features found to be statistically correlated with a change in aircraft performance, or those broadly describing the characteristics of an aircraft. Over time, more specific features may be used and as a ML model's performance improves, a set of the most relevant features may be identified.). Regarding claim 5, Gibbons discloses: wherein the one or more operational factors comprise one or more of a number of cycles, a type of landing, a type of airport, weather conditions experienced during flight, a load factor, or hours of flight ([0073], The collected data may be obtained from on-board sensors (e.g., airspeed, wind resistance, wind velocity, drag, etc.), ground-based systems (e.g., weather conditions, trajectory, etc.), satellites, or airlines records, for example. Once a suitable aircraft performance model has been developed for a specific type or class of aircraft (such as a Boeing 747), that model may be integrated with the TASAR system to provide a more accurate means of flight or trajectory planning for that type or class of aircraft. Such a model for a type or class of aircraft may be created for a plurality of types or classes, i.e., multiple airframes from each of several manufacturers.). Regarding claim 6, Gibbons discloses: performing, by the one or more processors, an action related to the aircraft based on the performance factor ([0098], Using that information, the system and methods are able to modify a standard or baseline APM to make it more specific to an aircraft and then use the modified APM in the TASAR system to generate more optimal flight plans for that aircraft). Regarding claim 7, Gibbons discloses: wherein the action comprises scheduling maintenance of the aircraft ([0068], The output may be, for example, predicted fuel milage performance, expected time to next maintenance, expected cost of operating per mile flown, etc.; [0075], Further, as will be described, data collected during the operation of each individual aircraft (each “tail”) may be used as part of a feedback loop to modify an aircraft performance model to make the model specific to the individual aircraft. This will further optimize the trajectory and flight planning data produced by the TASAR system for the individual aircraft. This is expected to lead to improvements in scheduling maintenance, improved fuel consumption, reduced repair costs over the lifetime of an aircraft, lowered operating costs, improved safety, and in some cases, even improved comfort for passengers during flights; [0102], In addition to more optimal flight planning, the aircraft specific information (as expressed by variations from a standard or baseline performance model) may be used to schedule maintenance and repair more effectively for the individual aircraft). Regarding claim 8, Gibbons discloses: generating, by the one or more processors, using the custom performance model, a flight plan ([0098], Using that information, the system and methods are able to modify a standard or baseline APM to make it more specific to an aircraft and then use the modified APM in the TASAR system to generate more optimal flight plans for that aircraft; Fig. 4); and uploading the flight plan to a Flight Management System (FMS) of the aircraft (Fig. 4; [0229], As shown in FIG. 4, system 400 may represent a server or other form of computing or data processing device. Modules 402 each contain a set of executable instructions, where when the set of instructions is executed by a suitable electronic processor (such as that indicated in the figure by “Physical Processor(s) 430”), system (or server or device) 400 operates to perform a specific process, operation, function or method. Modules 402 are stored in a memory 420, which typically includes an Operating System module 404 that contains instructions used (among other functions) to access and control the execution of the instructions contained in other modules. The modules 402 in memory 420 are accessed for purposes of transferring data and executing instructions by use of a “bus” or communications line 418, which also serves to permit processor(s) 430 to communicate with the modules for purposes of accessing and executing a set of instructions. Bus or communications line 418 also permits processor(s) 430 to interact with other elements of system 400, such as input or output devices 422, communications elements 424 for exchanging data and information with devices external to system 400, and additional memory devices 426.). Regarding claim 9, Gibbons discloses: estimating, by the one or more processors, using the custom performance model, fuel consumption for a planned flight of the aircraft ([0067], By collecting information for a sufficient number of aircraft of the same airframe type (or having other characteristic(s) common to the set of aircraft), a machine learning model can be trained to predict a different aircraft's fuel milage performance (or other characteristic) based on a set of features.). Regarding claim 10, Gibbons discloses: A system for assessing performance of an aircraft, the system comprising (Abstract, Systems, apparatuses, and methods for more effectively providing pilots with optimal suggested route or trajectory changes during flight.): a memory ([0010], In some embodiments, the methods include a process, method, function, or operation performed in response to the execution of a set of computer-executable instructions or software, where the instructions are stored in (or on) one or more non-transitory electronic data storage elements or memory. In some embodiments, the set of instructions may be conveyed to an aircraft or to a network element with which the aircraft is in communication from a remote server over a network. The set of instructions may be executed by an electronic processor or data processing element (e.g., CPU, GPU, controller, etc.). The data processing element may be contained in an on-board system, a remote server, a network element, a handheld device, or in some cases, another aircraft.); and processing circuitry coupled to the memory and configured to ([0010], In some embodiments, the methods include a process, method, function, or operation performed in response to the execution of a set of computer-executable instructions or software, where the instructions are stored in (or on) one or more non-transitory electronic data storage elements or memory. In some embodiments, the set of instructions may be conveyed to an aircraft or to a network element with which the aircraft is in communication from a remote server over a network. The set of instructions may be executed by an electronic processor or data processing element (e.g., CPU, GPU, controller, etc.). The data processing element may be contained in an on-board system, a remote server, a network element, a handheld device, or in some cases, another aircraft.): obtain flight data from one or more aircraft sensors, the flight data associated with a flight operation of an aircraft ([0074], The collected data may be obtained from on-board sensors (e.g., airspeed, wind resistance, wind velocity, drag, etc.), ground-based systems (e.g., weather conditions, trajectory, etc.), satellites, or airlines records, for example. Once a suitable aircraft performance model has been developed for a specific type or class of aircraft (such as a Boeing 747), that model may be integrated with the TASAR system to provide a more accurate means of flight or trajectory planning for that type or class of aircraft. Such a model for a type or class of aircraft may be created for a plurality of types or classes, i.e., multiple airframes from each of several manufacturers.); extract from the flight data, one or more flight data indicators, wherein each of the one or more flight data indicators represent performance of the aircraft during one or more flights (Abstract, The collected operational and performance data may be used as input data or “features” for a machine learning algorithm to generate a parameter of a performance model; [0023], collecting operational and performance data for each of a plurality of aircraft); correlate, by a machine learning model, the one or more flight data indicators with one or more operational factors (Abstract, The collected operational and performance data may be used as input data or “features” for a machine learning algorithm to generate a parameter of a performance model; [0024], training a machine learning model to output a parameter of an aircraft performance model from an input to the model, the input comprising operational or performance data for a different aircraft.); generate, by the machine learning model, a custom performance model for the aircraft based on the correlating of the one or more flight data indicators with the one or more operational factors ([0009], In some embodiments, deviations from the performance “predicted” or expected using a baseline aircraft performance model may be determined for an individual aircraft (such as flight time, fuel consumption, drag, lift, etc.). The deviations may be used in a process to update or revise the baseline performance model used in trajectory planning for that aircraft or for a similar set of aircraft. This enables TASAR to more accurately “predict” flight performance and provide more effective route planning. In some embodiments, collection of a suitable set of data from multiple aircraft and the training of a machine learning model may enable the system described herein to identify the characteristics of an aircraft that have the most significant impact on the baseline model parameters, and hence on the trajectory planning process.); output a performance factor for the aircraft based on the custom performance model ([0024], output a parameter of an aircraft performance model from an input to the model, the input comprising operational or performance data for a different aircraft). Regarding claim 12, Gibbons discloses: wherein the performance factor comprises at least one of: estimated flight time of the aircraft, evaluation of fuel efficiency of the aircraft, identification of one or more safety risks of the aircraft, and analysis of operational trends of the aircraft ([0073], performance parameters (such as fuel consumption, lift, drag, etc.)). Regarding claim 13, Gibbons discloses: wherein the performance factor forecasts potential aircraft performance issues based on current trends and the one or more operational factors ([0067], By collecting information for a sufficient number of aircraft of the same airframe type (or having other characteristic(s) common to the set of aircraft), a machine learning model can be trained to predict a different aircraft's fuel milage performance (or other characteristic) based on a set of features. For example, a set of data may be collected for each aircraft of the same manufacturer and airframe model (such as a Boeing 747) that includes information on multiple aspects of each aircraft (type of routes flown, miles flown, years in service, performed maintenance, etc.) and its performance (fuel mileage, frequency of repair, nature of repairs, service issues, etc.). A set of this data for multiple aircraft can be used as training data for a machine learning (Mir) model or models. In some examples, the data may be for aircraft of different types that have similar characteristics (such as wingspan, weight, operating altitude, etc.); [0068], Each ML model may be trained to output a prediction or expected value of a specific characteristic of an aircraft whose feature data is used as an input to the trained model. The output may be, for example, predicted fuel milage performance, expected time to next maintenance, expected cost of operating per mile flown, etc. For example a trained model might be used to “predict” how the performance of an individual aircraft or of a set of aircraft would be expected to change over a specific time period or based on the number of miles flown, the number of takeoffs and landings, etc. As another example, a model might be trained to predict the expected drag coefficient for an airframe based on age and/or miles flown. The features on which a ML model is trained may be a subset of the data collected for a group of aircraft. This subset may be those features found to be statistically correlated with a change in aircraft performance, or those broadly describing the characteristics of an aircraft. Over time, more specific features may be used and as a ML model's performance improves, a set of the most relevant features may be identified). Regarding claim 14, Gibbons discloses: wherein the one or more operational factors comprise one or more of a number of cycles, a type of landing, a type of airport, weather conditions experienced during flight, a load factor, or hours of flight ([0073], The collected data may be obtained from on-board sensors (e.g., airspeed, wind resistance, wind velocity, drag, etc.), ground-based systems (e.g., weather conditions, trajectory, etc.), satellites, or airlines records, for example. Once a suitable aircraft performance model has been developed for a specific type or class of aircraft (such as a Boeing 747), that model may be integrated with the TASAR system to provide a more accurate means of flight or trajectory planning for that type or class of aircraft. Such a model for a type or class of aircraft may be created for a plurality of types or classes, i.e., multiple airframes from each of several manufacturers.). Regarding claim 15, Gibbons discloses: the processing circuitry further configured to: perform an action related to the aircraft based on the performance factor ([0098], Using that information, the system and methods are able to modify a standard or baseline APM to make it more specific to an aircraft and then use the modified APM in the TASAR system to generate more optimal flight plans for that aircraft). Regarding claim 16, Gibbons discloses: wherein the action comprises scheduling maintenance of the aircraft ([0068], The output may be, for example, predicted fuel milage performance, expected time to next maintenance, expected cost of operating per mile flown, etc.; [0075], Further, as will be described, data collected during the operation of each individual aircraft (each “tail”) may be used as part of a feedback loop to modify an aircraft performance model to make the model specific to the individual aircraft. This will further optimize the trajectory and flight planning data produced by the TASAR system for the individual aircraft. This is expected to lead to improvements in scheduling maintenance, improved fuel consumption, reduced repair costs over the lifetime of an aircraft, lowered operating costs, improved safety, and in some cases, even improved comfort for passengers during flights; [0102], In addition to more optimal flight planning, the aircraft specific information (as expressed by variations from a standard or baseline performance model) may be used to schedule maintenance and repair more effectively for the individual aircraft). Regarding claim 17, Gibbons discloses: generate, using the custom performance model, a flight plan ([0098], Using that information, the system and methods are able to modify a standard or baseline APM to make it more specific to an aircraft and then use the modified APM in the TASAR system to generate more optimal flight plans for that aircraft; Fig. 4); and upload the flight plan to a Flight Management System (FMS) of the aircraft (Fig. 4; [0229], As shown in FIG. 4, system 400 may represent a server or other form of computing or data processing device. Modules 402 each contain a set of executable instructions, where when the set of instructions is executed by a suitable electronic processor (such as that indicated in the figure by “Physical Processor(s) 430”), system (or server or device) 400 operates to perform a specific process, operation, function or method. Modules 402 are stored in a memory 420, which typically includes an Operating System module 404 that contains instructions used (among other functions) to access and control the execution of the instructions contained in other modules. The modules 402 in memory 420 are accessed for purposes of transferring data and executing instructions by use of a “bus” or communications line 418, which also serves to permit processor(s) 430 to communicate with the modules for purposes of accessing and executing a set of instructions. Bus or communications line 418 also permits processor(s) 430 to interact with other elements of system 400, such as input or output devices 422, communications elements 424 for exchanging data and information with devices external to system 400, and additional memory devices 426.). Regarding claim 18, Gibbons discloses: estimate, using the custom performance model, fuel consumption for a planned flight of the aircraft ([0067], By collecting information for a sufficient number of aircraft of the same airframe type (or having other characteristic(s) common to the set of aircraft), a machine learning model can be trained to predict a different aircraft's fuel milage performance (or other characteristic) based on a set of features.). Regarding claim 19, Gibbons discloses: Non-transitory computer-readable storage media having instructions encoded thereon, the instructions configured to cause processing circuitry to ([0010], In some embodiments, the methods include a process, method, function, or operation performed in response to the execution of a set of computer-executable instructions or software, where the instructions are stored in (or on) one or more non-transitory electronic data storage elements or memory. In some embodiments, the set of instructions may be conveyed to an aircraft or to a network element with which the aircraft is in communication from a remote server over a network. The set of instructions may be executed by an electronic processor or data processing element (e.g., CPU, GPU, controller, etc.). The data processing element may be contained in an on-board system, a remote server, a network element, a handheld device, or in some cases, another aircraft.): obtain flight data from one or more aircraft sensors, the flight data associated with a flight operation of an aircraft ([0074], The collected data may be obtained from on-board sensors (e.g., airspeed, wind resistance, wind velocity, drag, etc.), ground-based systems (e.g., weather conditions, trajectory, etc.), satellites, or airlines records, for example. Once a suitable aircraft performance model has been developed for a specific type or class of aircraft (such as a Boeing 747), that model may be integrated with the TASAR system to provide a more accurate means of flight or trajectory planning for that type or class of aircraft. Such a model for a type or class of aircraft may be created for a plurality of types or classes, i.e., multiple airframes from each of several manufacturers.); extract from the flight data, one or more flight data indicators, wherein each of the one or more flight data indicators represent performance of the aircraft during one or more flights (Abstract, The collected operational and performance data may be used as input data or “features” for a machine learning algorithm to generate a parameter of a performance model; [0023], collecting operational and performance data for each of a plurality of aircraft); correlate, by a machine learning model, the one or more flight data indicators with one or more operational factors (Abstract, The collected operational and performance data may be used as input data or “features” for a machine learning algorithm to generate a parameter of a performance model; [0024], training a machine learning model to output a parameter of an aircraft performance model from an input to the model, the input comprising operational or performance data for a different aircraft.); generate, by the machine learning model, a custom performance model for the aircraft based on the correlating of the one or more flight data indicators with the one or more operational factors ([0009], In some embodiments, deviations from the performance “predicted” or expected using a baseline aircraft performance model may be determined for an individual aircraft (such as flight time, fuel consumption, drag, lift, etc.). The deviations may be used in a process to update or revise the baseline performance model used in trajectory planning for that aircraft or for a similar set of aircraft. This enables TASAR to more accurately “predict” flight performance and provide more effective route planning. In some embodiments, collection of a suitable set of data from multiple aircraft and the training of a machine learning model may enable the system described herein to identify the characteristics of an aircraft that have the most significant impact on the baseline model parameters, and hence on the trajectory planning process.); and output a performance factor for the aircraft based on the custom performance model ([0024], output a parameter of an aircraft performance model from an input to the model, the input comprising operational or performance data for a different aircraft) . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-21-aia AIA Claim s 2, 11, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Gibbons in view of Nam et al. (US 20260003878 A1), hereinafter Nam . Regarding claim 2, Gibbons does not specifically state: wherein the machine learning model comprises a relational model. However, Nam teaches: wherein the machine learning model comprises a relational model (Fig. 6; [0009], one or more processors, and the at least one program receives mixed data including relational data about information for identifying an object and spatiotemporal data about a trajectory of the object moving in a target space, discretizes the relational data and the spatiotemporal data based on a level of detail that is preset for each designated area of the target space corresponding to the trajectory of the object, and generates a mixed learning model that learns the relational data and the spatiotemporal data for each level of detail using multiple relational models and spatiotemporal models; [0015], Here, the at least one program may learn the relational model only when there is a change in a correlation between variables by checking the correlation each time new data is input in a process of learning the relational model. [0020], Here, discretizing the relational data and the spatiotemporal data may comprise performing transformation into three-dimensional (3D) spatial data about time, a space, and a trajectory of the spatiotemporal data.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Nam into the invention of Gibbons to include utilizing a relational machine learning model as Nam discloses with a reasonable expectation of success. One would be motivated to incorporate aspects of the cited prior art to create a more robust system that can preserve and capture relationships present in various data tables generated by interconnected sensors to predict vehicle pathing (Nam: [0003]). Additionally, the claimed invention is merely a combination of old, well-known elements of aircraft performance machine learning model to capture performance difference over time as disclosed by Gibbons and relational models to capture and predict vehicle pathing as taught by Nam. The combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable. Claim 11 is rejected under similar rationale as claim 2. Claim 20 is rejected under similar rationale as claim 2. Documents Considered but Not Considered 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Moeykens (US 11417154 B1) discloses a system for electric aircraft fleet management for at least an electric aircraft is provided. the system includes a computing device communicatively connected to at least an electric aircraft, wherein the computing device is configured to receive a plurality of measured aircraft operation datum from a sensor disposed on the at least an electric aircraft, select a training set as a function of each measured aircraft operation datum of the plurality of measured aircraft operation datum and the at least an electric aircraft, wherein each measured aircraft operation datum of the plurality of measured aircraft operation datum is correlated to an element of modeled aircraft data, and generate, using a machine-learning algorithm, an aircraft performance model output based on the plurality of measured aircraft operation datum and the selected training set, wherein generating an aircraft performance model includes generating a performance alert . Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to IZCALLI ANDRE RIOS-AGUIRRE whose telephone number is (571)272-0790. The examiner can normally be reached Monday through Friday 8:30 - 17:00 EST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott A. Browne can be reached at (571) 270-0151. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /I.A.R./ Examiner, Art Unit 3666 /SCOTT A BROWNE/ Supervisory Patent Examiner, Art Unit 3666 Application/Control Number: 19/183,538 Page 2 Art Unit: 3666 Application/Control Number: 19/183,538 Page 3 Art Unit: 3666 Application/Control Number: 19/183,538 Page 4 Art Unit: 3666 Application/Control Number: 19/183,538 Page 5 Art Unit: 3666 Application/Control Number: 19/183,538 Page 6 Art Unit: 3666 Application/Control Number: 19/183,538 Page 7 Art Unit: 3666 Application/Control Number: 19/183,538 Page 8 Art Unit: 3666 Application/Control Number: 19/183,538 Page 9 Art Unit: 3666 Application/Control Number: 19/183,538 Page 10 Art Unit: 3666 Application/Control Number: 19/183,538 Page 11 Art Unit: 3666 Application/Control Number: 19/183,538 Page 12 Art Unit: 3666 Application/Control Number: 19/183,538 Page 13 Art Unit: 3666 Application/Control Number: 19/183,538 Page 14 Art Unit: 3666 Application/Control Number: 19/183,538 Page 15 Art Unit: 3666 Application/Control Number: 19/183,538 Page 16 Art Unit: 3666 Application/Control Number: 19/183,538 Page 17 Art Unit: 3666 Application/Control Number: 19/183,538 Page 18 Art Unit: 3666 Application/Control Number: 19/183,538 Page 19 Art Unit: 3666 Application/Control Number: 19/183,538 Page 20 Art Unit: 3666 Application/Control Number: 19/183,538 Page 21 Art Unit: 3666 Application/Control Number: 19/183,538 Page 22 Art Unit: 3666 Application/Control Number: 19/183,538 Page 23 Art Unit: 3666 Application/Control Number: 19/183,538 Page 24 Art Unit: 3666 Application/Control Number: 19/183,538 Page 25 Art Unit: 3666 Application/Control Number: 19/183,538 Page 26 Art Unit: 3666 Application/Control Number: 19/183,538 Page 27 Art Unit: 3666 Application/Control Number: 19/183,538 Page 28 Art Unit: 3666 Application/Control Number: 19/183,538 Page 29 Art Unit: 3666 Application/Control Number: 19/183,538 Page 30 Art Unit: 3666 Application/Control Number: 19/183,538 Page 31 Art Unit: 3666 Application/Control Number: 19/183,538 Page 32 Art Unit: 3666
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